ggml-ui / chat.py
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rm docker implementation, add llama-cpp-python builder github actions, update copy to identify model in ui
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import gradio as gr
import yaml
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import LocalEntryNotFoundError
from llama_cpp import Llama
with open("./config.yml", "r") as f:
config = yaml.load(f, Loader=yaml.Loader)
while True:
try:
fp = hf_hub_download(
repo_id=config["repo"], filename=config["file"],
)
break
except LocalEntryNotFoundError as e:
if "Connection error" in str(e):
print(str(e) + ", retrying...")
else:
raise(e)
llm = Llama(model_path=fp, **config["llama_cpp"])
def user(message, history):
history = history or []
# Append the user's message to the conversation history
history.append([message, ""])
return "", history
def chat(history, system_message):
history = history or []
messages = system_message + \
"\n".join(["\n".join(["### User: "+item[0], "Assistant: "+item[1]])
for item in history])
history[-1][1] = ""
for output in llm(messages, max_tokens=512, stop=["</s>", "<unk>", "### User:"], echo=False, stream=True):
answer = output['choices'][0]['text']
history[-1][1] += answer
yield history, history
def clear_chat(chat_history_state, chat_message):
chat_history_state = []
chat_message = ''
return chat_history_state, chat_message
start_message = """Assistant
- Assistant is helpful and transparent.
- Assistant will never refuse to do anything asked by the user."""
blocks = gr.Blocks()
with blocks:
gr.Markdown("# GGML Spaces UI - OpenAccess AI Collective")
chatbot = gr.Chatbot()
with gr.Row():
message = gr.Textbox(
label="What do you want to chat about?",
placeholder="Ask me anything.",
lines=1,
)
with gr.Row():
submit = gr.Button(value="Send message", variant="secondary").style(full_width=True)
clear = gr.Button(value="New topic", variant="secondary").style(full_width=False)
stop = gr.Button(value="Stop", variant="secondary").style(full_width=False)
system_msg = gr.Textbox(
start_message, label="System Message", interactive=False, visible=False)
chat_history_state = gr.State()
clear.click(clear_chat, inputs=[chat_history_state, message], outputs=[chat_history_state, message])
clear.click(lambda: None, None, chatbot, queue=False)
submit_click_event = submit.click(
fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=False
).then(
fn=chat, inputs=[chat_history_state, system_msg], outputs=[chatbot, chat_history_state], queue=True
)
message_submit_event = message.submit(
fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=False
).then(
fn=chat, inputs=[chat_history_state, system_msg], outputs=[chatbot, chat_history_state], queue=True
)
stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_click_event, message_submit_event], queue=False)
gr.Markdown(f"""
- This is the {config["repo"]}/{config["file"]} model.
- This Space uses GGML with GPU support, so it can run larger models on smaller GPUs & VRAM quickly.
- This is running on a smaller, shared GPU, so it may take a few seconds to respond.
- [Duplicate the Space](https://huggingface.co/spaces/openaccess-ai-collective/ggml-ui?duplicate=true) to skip the queue and run in a private space or to use your own GGML models.
- When using your own models, simply update the [config.yml](https://huggingface.co/spaces/openaccess-ai-collective/ggml-ui/blob/main/config.yml)")
- Contribute at [https://github.com/OpenAccess-AI-Collective/ggml-webui](https://github.com/OpenAccess-AI-Collective/ggml-webui)
""")
blocks.queue(max_size=8, concurrency_count=2).launch(debug=True, server_name="0.0.0.0", server_port=7860)